COVID-19 is an emerging, rapidly evolving situation.

Get the latest public health information from CDC and research information from NIH.

U.S. flag

An official website of the United States government

Dot gov

The .gov means it's official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.


The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Share This:

Integrating Drug's Mode of Action into Quantitative Structure-Activity Relationships for Improved Prediction of Drug-Induced Liver Injury

Leihong Wu, Zhichao Liu, Scott Auerbach, Ruili Huang, Minjun Chen, Kristin McEuen, Joshua Xu, Hong Fang, and Weida Tong.
Journal of Chemical Information and Modeling (2017) DOI: PMID: 28350954



Drug-induced liver injury (DILI) is complex in mechanism. Different drugs could undergo different mechanisms but result in the same DILI type, while the same drug could lead to different DILI types via different mechanisms. Therefore, predicting a drug's potential for DILI should take its underlying mechanisms into consideration. To achieve that, we constructed a novel approach by incorporating the drug's Mode of Action (MOA) into Quantitative Structure-Activity Relationship (QSAR) modeling. This MOA-DILI approach was examined using a data set of 333 drugs. The drugs were first grouped according to their MOA profiles (positive or negative in each MOA) based on the Tox21 qHTS assays. QSAR models for individual MOA assays were developed and subsequently combined to obtain the MOA-DILI model. A hold-out testing strategy (222 drugs for training and 111 drugs as a test set) was employed, which yielded a predictive accuracy of 0.711. The MOA-DILI model was directly compared with the standard QSAR approach using the same hold-out strategy, and the QSAR model yielded an accuracy of 0.662. To minimize the random chance in splitting training/test sets, the hold-out testing process was repeated 1000 times, and the observed difference in prediction accuracy between MOA-DILI and QSARs was statistically significant (P value <0.0001). Out of 17 MOAs used, four assays (i.e., antioxidant response elements, PPAR-gamma, estrogen receptor, and thyroid receptor assays) contributed most to the improved prediction of the MOA-DILI model over QSARs. In conclusion, the MOA-DILI approach has the potential to significantly improve predictive outcomes and to reveal complex relationships between MOAs and DILI, all of which would be helpful in developing DILI predictive models in drug screening and for risk assessment of industrial chemicals.


Figure 1. Overview of MOA-DILI modeling process.

A total of 333 drugs were randomly divided into a training set (222 drugs) for model development and a test set (111 drugs) for hold-out testing, and this process was repeated 1000 times to generate 1000 pairs of training/test sets. For each of 17 MOAs from Tox21 assays, the training set of drugs was split into two groups, one active in the assay and the other inactive in the assay. The 5-fold cross-validation was performed on each group, and the results were combined to assess the prediction accuracy of each QSARMOA model. A sequential forward selection procedure was applied to assess the performance of the combinations of QSARMOA models, and the best combination was namely the MOA-DILI model. The MOA-DILI was then evaluated using the hold-out test set, and its performance was compared with the standard QSAR model using the same training set.

Figure 2. Distribution of the number of QSARMOA models used in 1000 MOA-DILI models.

Most MOA-DILI models used an even number of MOAs.


Table 1. Performance of QSARMOA Model and Assay Alone Predictions.

Table 2. Overall Performance of MOA-DILI Model in Training and Test Sets.

Supplemental Materials

Supporting Information